Stability and adaptability of grain sorghum hybrids in the off-season

Genotype by environment interaction, AMMI, GGE biplot, and mega environment analysis of elite Sorghum bicolor (L.) Moench genotypes in humid lowland areas of Ethiopia

This study aimed to evaluate high-yielding, stable sorghum genotypes and determine the ideal (representative and discriminating) testing environments for genotypes in the humid lowlands of Ethiopia. A total of forty-two sorghum genotypes were used for a field trial conducted in six different environments using a randomized complete block design. Yield stability, Additive main effect, multiplicative interaction (AMMI), and genotype and genotype by environment interaction (GGE) were computed. The AMMI analysis explained 62.85% of the G×E variance. The AMMI1 biplot revealed that (G4; Mok079 and (G16; Ba066) genotypes had higher grain yields. AMMI2 biplot suggested that genotypes (G18; Y0470),(G23;100620), (G29; PML981475), and (G11; ETSC300373-4) show higher sensitivity to environmental changes because of their strong genotype-by-environment interactions. The GGE captured 79.46% of the GGE variance, and the GGE biplot identified genotypes (G4; Mok079), (G10; Sl081) and (G16; Ba066) were the most stable genotypes whereas(G39; ETSC120051-3) was the least stable genotypes. The GGE biplot identified Assosa (AS20) as a suitable environment, whereas PW20 and JM20 were the most discriminating and non-representative environments. The GGE biplot was found to identify three main mega-environments for sorghum growing in the humid lowlands of Ethiopia., both the AMMI and GGE biplots revealed (G4; Mok079) had the highest level of adaptability to all tested environments and was approved by the National Variety Release Committee for release in 2022.

Development of Sorghum Genotypes for Improved Yield and Resistance to Grain Mold Using Population Breeding Approach

The infection caused by grain mold in rainy season grown sorghum deteriorates the physical and chemical quality of the grain, which causes a reduction in grain size, blackening, and making them unfit for human consumption. Therefore, the breeding for grain mold resistance has become a necessity. Pedigree breeding has been widely used across the globe to tackle the problem of grain mold. In the present study, a population breeding approach was employed to develop genotypes resistant to grain mold. The complex genotype × environment interactions (GEIs) make the task of identifying stable grain mold-resistant lines with good grain yield (GY) challenging. In this study, the performance of the 33 population breeding derivatives selected from the four-location evaluation of 150 genotypes in 2017 was in turn evaluated over four locations during the rainy season of 2018. The Genotype plus genotype-by-environment interaction (GGE) biplot analysis was used to analyze a significant GEI observed for GY, grain mold resistance, and all other associated traits. For GY, the location explained a higher proportion of variation (51.7%) while genotype (G) × location (L) contributed to 21.9% and the genotype contributed to 11.2% of the total variation. For grain mold resistance, G × L contributed to a higher proportion of variation (30.7%). A graphical biplot approach helped in identifying promising genotypes for GY and grain mold resistance. Among the test locations, Dharwad was an ideal location for both GY and grain mold resistance. The test locations were partitioned into three clusters for GY and two clusters for grain mold resistance through a "which-won-where" study. Best genotypes in each of these clusters were selected. The breeding for a specific cluster is suggested. Genotype-by-trait biplots indicated that GY is influenced by flowering time, 100-grain weight (HGW), and plant height (PH), whereas grain mold resistance is influenced by glume coverage and PH. Because GY and grain mold score were independent of each other, there is a scope to improve both yield and resistance together.